Design of fuzzzy pid controller for bldc motor


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Design of Fuzzy PID controller for BRUSHLESS D.C MOTORS
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Design of fuzzzy pid controller for bldc motor

  1. 1. Topic Design of Fuzzy PID controller for BRUSHLESS D.C MOTORS
  2. 2. Working of BLDC -MOTOR As there is no commutator ,the current direction of the conductor on the stator controlled electronically. Rotor consists the permanent magnet where as stator consist a no. of windings. Current through these winding produces magnetic field and force. Hall sensor used to determine the position during commutation.
  3. 3. Working Procedure… • Each sequence has •one winding energized positive (current into the winding) •one winding energized negative (current out of the winding) •one winding non-energized •When a rotor pole passes a Hall-Effect sensor, get a high or low signal, indicating that a North or South pole
  4. 4. Block Diagram of speed control of BLDC Motor
  5. 5. Why we need controller? • In close loop response four characteristics are important • 1)Rise time: the time it takes for the plant output to rise beyond 90% of the desired level for the first time. • 2)Overshoot time : how much the peak level is higher than the steady state, normalized against the steady state. • 3)Settling time: the time it takes for the system to converge to its steady state. • 4) Steady state error: The difference between the steady-state output and the desired output. • We wanted the PID controller to satisfy the following criteria: ▫ Settling time --less ▫ Overshoot and rise time --less ▫ Steady-state error less than 1%
  6. 6. Conventional PID controller: • Proportional-integral-derivative (PID) control provides a generic and efficient solution to real world control problems It is used to eliminate error: • Error is defined as the difference between set-point and measurement. (error) = (set-point) - (measurement) • The output of PID controller will change in response to the error •
  7. 7. • A PID controller is simple three-term controller Typical steps for designing a PID controller are • i) Determine what characteristics of the system needs to be improved. • ii) Use KP to decrease the rise time. • iii) Use KD to reduce the overshoot and settling time. • iv) Use KI to eliminate the steady-state error.
  8. 8. Disadvantage: • The design of the BLDCM drive involves a complex process such as modeling, control scheme selection, simulation and parameters tuning etc PID controller working is not good for non-linear and complex systems. • Fuzzy PID control method is a better method of controlling, to the complex and unclear model systems, it can give simple and effective control
  9. 9. Fuzzy logic: • Fuzzy logic is a form of many-valued logic or probabilistic logic; it deals with reasoning that is approximate rather than fixed and exact. • Fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. • Fuzzy logic can be described simply as "computing with words rather than numbers"
  10. 10. Fuzzy logic includes: • 1)Fuzzy Set: A fuzzy set is collection of related items which belong to that set to different degrees in interval [0,1]. • 2)Fuzzy rules: • In fuzzy logic these rules are used to make decisions. • IF-THEN rules : ―IF temperature very cold THEN stop fan‖ • AND, OR, and NOT operators : • ―IF temperature IS hot AND pressure IS low, THEN fan ON‖ . NOT x = (1 - truth(x)) x AND y = minimum(truth(x), truth(y)) x OR y = maximum(truth(x), truth(y))
  11. 11. Linguistic Variables: Variables whose values are words or sentences in human language are called linguistic variables Example: For the case of motor, speed can be taken as linguistic variable as:
  12. 12. Membership functions A set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. Triangular Bell-shaped Gussian shape
  13. 13. Diagrammatic representation of motor speed in terms of linguistic expression
  14. 14. Fuzzy logic controller (FLC): • A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1. • Fuzzy control is based on fuzzy logic, a logical system which is much closer to human thinking and natural language than traditional logical systems • Fuzzy control can be described simply as "control with sentences rather than equations"
  15. 15. Fuzzy logic controller: • A fuzzy controller can include empirical rules, and that is especially useful in operator controlled plants. It follows following processes
  16. 16. Fuzzification • Every crisp value of input we attribute a set of degrees of membership to fuzzy sets defined in the universe of discourse for that input. It measure the values of input variable. It performs scale mapping that transfers the range of values of input variables into their corresponding universe of discourse(fuzzy set) or into degree of membership.
  17. 17. Inference: • The fuzzy IF-THEN rule expresses a fuzzy implication relation between the fuzzy sets of the premise and the fuzzy sets of the conclusion. It includes decision logic operators such as OR, AND ALSO etc • IF LOAD INCREASES THEN SPEED REMAIN CONSTANT • It includes: Matching of the facts with the rule premises. Implication The next step is the determination of the individual rule output. Aggregation: The collective sum of each rule is obtained in this step.
  18. 18. Defuzzification: This process to obtain crisp output from fuzzy sets is called defuzzification. It is the reverse process of fuzzification. In fuzzy controller don’t require equations , its algorithm is rules that is made by human. Fuzzy controller make decisions automatically according to these rules.
  19. 19. Fuzzy logic in MATLAB: Fuzzy logic is a problem-solving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstation-based data acquisition and control systems. It can be implemented in hardware, software, or a combination of both. Inputs Rules Output
  20. 20. Simulation of Fuzzy PID controller: PD
  21. 21. Characteristics of motor, 1500 rpm with no load (a) Speed
  22. 22. Characteristics of motor, 1500 rpm with load (a) Speed
  23. 23. Step up Characteristics of motor,10001500 rpm with no load (a) Speed
  24. 24. Step down Characteristics of motor,15001000 rpm with no load (a) Speed
  25. 25. Step down Characteristics of motor,15001000 rpm with load (a) Speed
  26. 26. COMPARISON: • It can work with less precise inputs. • It does not need fast processors. • Tuning of fuzzy PID controller is easy ,more robust than other non-linear controllers. • Fuzzy controllers have better stability, small overshoot, and fast response. Fuzzy PID CONTROLLER • Conventional PID controller algorithm is simple, stable, easy adjustment and high reliability. • It does not require processor • Tuning PID control parameters is very difficult, poor robustness, therefore, it's difficult to achieve the optimal state under field conditions in the actual production • When load varies it becomes unstable, give more overshoot. PID CONTROLLER
  27. 27. Limitations of fuzzy PID controller: • · Require more fine tuning and simulation before operational. • If the a reliable expert knowledge is not Available , or If the controlled system is too complex to derive the required decision rules, development of a fuzzy logic controller become time consuming and tedious or sometimes impossible. • An fuzzy logic controller cannot be achieved by trial and- error
  28. 28. Fuzzy PID controller reduces overshoot very significantly as compare to conventional PID controller . Similarly fuzzy PID controller gives more robust with change of load and speed but PID controller produces do not robust sudden change in speed and load. So, Fuzzy PID controller is better than simple PID.
  29. 29. References: • [1] Q.D.Guo,X.MZhao. BLDC motor principle and technology application [M]. Beijing: China electricity press,2008. • [2] Chuen Chien Lee, ―Fuzzy Logic in Control Systems:Fuzzy Logic controller–Part 1‖ 1990 IEEE • [3] Chuen Chien Lee, ―Fuzzy Logic in Control Systems : Fuzzy Logic controller Part 2‖ 1990 IEEE . • [4]Comparison between Conventional and Fuzzy Logic PID Controllers for Controlling DC Motors by IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010 • [5]Design of Fuzzy PID Controller for Brushless DC Motor 2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA